Study on the extension of the dynamic benchmark system of per capita carbon emissions in China’s county

被引:0
|
作者
Fengmei Yang
Longyu Shi
Xiaotong Wang
Lijie Gao
机构
[1] Chinese Academy of Sciences,Key Laboratory of Urban Environment and Health, Institute of Urban Environment
[2] University of Chinese Academy of Sciences,undefined
[3] Xiamen Institute of Technology,undefined
关键词
Carbon intensity reduction target allocation; Capacity-responsibility; County; Per capita carbon emissions; Dynamic benchmark;
D O I
暂无
中图分类号
学科分类号
摘要
County is the center of China’s socio-economic development and the key node for urban–rural integration. Also, the county is an important carrier for promoting urban and rural green development. Improving green and low-carbon development capabilities and formulating county-level low-carbon standards will play a significant role in promoting China’s new people-oriented urbanization and rural revitalization. Although there have been extensive studies on low-carbon benchmarks, over half of the benchmarks tend to ignore the development stage of the evaluated region and its needs. When the region’s economy reaches a certain level, constraints from low-carbon targets may limit the local development process. This study firstly allocated county carbon intensity reduction targets (CIRT) by considering the differences in county carbon reduction capacity and responsibility. Secondly, a dynamic benchmark system of per capita carbon emissions (PCCE) in counties in China is constructed. Finally, we took Changxing County in Zhejiang Province as a research case to verify the dynamic benchmark of PCCE. According to the carbon intensity target reduction rate (CITRR), China’s counties can be divided into three categories: low carbon emissions reduction capability-responsible counties (L-CERCRC), medium carbon emissions reduction capability-responsible counties (M-CERCRC), and high carbon emissions reduction capability-responsible counties (H-CERCRC). The results show that (1) due to the national CO2 emission reduction target in 2030, the carbon intensity will be 60% lower than in 2005, the CITRR for China’s 1510 counties range from 8.36 to 137.83%; the average CITRR is 48.40%. (2) Changxing County’s CITRR is 57.71%, which belongs to the H-CERCRC. The PCCE of Changxing County will be much higher than the benchmark when the carbon peak is reached in the future. (3) For reaching the aiming benchmark, Changxing County is suggested to adjust its relevant influencing factors of PCCE for converting local’s PCCE reaching to the benchmark within a certain time period. The dynamic benchmark system for PCCE in China’s counties established in this study is economically sensitive, which not only takes the differences of counties into account, but also meets the needs of counties’ diverse development form stages. This system provides counties a few coordinated directions which can improve the local’s economic development and reduce greenhouse gas (GHGs) emissions through the development progress.
引用
收藏
页码:10256 / 10271
页数:15
相关论文
共 50 条
  • [31] A Study on the Construction of China's Carbon Emissions Trading System
    Cheng Chengping
    Zhang Xu
    [J]. 2010 INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT AND DEVELOPMENT (ICEED2010), 2011, 5 : 1037 - 1043
  • [32] Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model
    Dong, Feng
    Long, Ruyin
    Chen, Hong
    Li, Xiaohui
    Yang, Qingliang
    [J]. PLOS ONE, 2013, 8 (12):
  • [33] The Convergence of Sulphur Dioxide (SO2) Emissions Per Capita in China
    Zhang, Yu-Chen
    Si, Deng-Kui
    Zhao, Bing
    [J]. SUSTAINABILITY, 2020, 12 (05) : 1 - 33
  • [34] The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions
    Wang, Qiang
    Li, Lejia
    [J]. SUSTAINABLE PRODUCTION AND CONSUMPTION, 2021, 28 : 760 - 774
  • [35] Does renewable energy reduce per capita carbon emissions and per capita ecological footprint? New evidence from 130 countries
    Li, Rongrong
    Wang, Qiang
    Li, Lejia
    [J]. ENERGY STRATEGY REVIEWS, 2023, 49
  • [36] A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China
    Qu, Jiansheng
    Qin, Shanshan
    Liu, Lina
    Zeng, Jingjing
    Bian, Yue
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2016, 23 (07) : 6430 - 6442
  • [37] A hybrid study of multiple contributors to per capita household CO2 emissions (HCEs) in China
    Jiansheng Qu
    Shanshan Qin
    Lina Liu
    Jingjing Zeng
    Yue Bian
    [J]. Environmental Science and Pollution Research, 2016, 23 : 6430 - 6442
  • [38] Convergence of per capita carbon dioxide emissions: implications and meta-analysis
    Acar, Sevil
    Soderholm, Patrik
    Brannlund, Runar
    [J]. CLIMATE POLICY, 2018, 18 (04) : 512 - 525
  • [39] Club convergence in per capita carbon dioxide emissions across Indian states
    Akram, Vaseem
    Rath, Badri Narayan
    Sahoo, Pradipta Kumar
    [J]. ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2024, 26 (08) : 19907 - 19934
  • [40] Energy use and the role of per capita income on carbon emissions in African countries
    Adeleye, Bosede Ngozi
    Osabohien, Romanus
    Lawal, Adedoyin Isola
    De Alwis, Tyrone
    [J]. PLOS ONE, 2021, 16 (11):